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Recent theoretical literature has suggested a variety of mechanisms through which poverty may deter growth and become self-perpetuating. A few papers have searched for empirical regularities consistent with those mechanisms - such as aggregate non-convexities and convergence clubs. However, a seemingly basic implication of the theoretical models, namely that countries suffering from higher levels of poverty should grow less rapidly, has remained untested. This paper attempts to fill that gap and provide a direct empirical assessment of the impact of poverty on growth. The paper's strategy involves including poverty indicators among the explanatory variables in an otherwise standard empirical growth equation. Using a large panel dataset, the authors find that poverty has a negative impact on growth that is significant both statistically and economically. This result is robust to a variety of specification changes, including (i) different poverty lines; (ii) different poverty measures; (iii) different sets of control variables; (iv) different estimation methods; (v) adding inequality as a control variable; and (vi) allowing for nonlinear effects of inequality on growth. The paper also finds evidence that the adverse effect of poverty on growth works through investment: high poverty deters investment, which in turn lowers growth. Further, the data suggest that this mechanism only operates at low levels of financial development, consistent with the predictions of theoretical models that underscore financial market imperfections as a key ingredient of poverty traps.

Capital investment --- Country case --- Credit constraints --- Debt Markets --- Development research --- Economic opportunities --- Economic Theory and Research --- Empirical estimates --- Empirical regularities --- Empirical studies --- Explanatory variables --- Finance and Financial Sector Development --- Financial development --- Growth equation --- Growth process --- Growth rates --- High poverty --- Human capital --- Inequality --- Macroeconomics and Economic Growth --- Negative impact --- Persistent poverty --- Policy research --- Poverty lines --- Poverty Monitoring and Analysis --- Poverty Reduction --- Poverty traps --- Pro-Poor Growth --- Rural Development --- Rural Poverty Reduction --- Services and Transfers to Poor

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July 2000 - In developing countries, increases in current account deficits tend to be associated with a rise in domestic output growth and shocks that increase the terms of trade and cause the real exchange rate to appreciate. Higher savings rates, higher growth rates in industrial economies, and higher international interest rates tend to have the opposite effect. Calderon, Chong, and Loayza examine the empirical links between current account deficits and a broad set of economic variables proposed in the literature. To accomplish this, they complement and extend previous research by using a large, consistent set of macroeconomic data on public and private domestic savings, external savings, and national income variables; focusing on developing economies by drawing on a panel data set for 44 developing countries and annual information for the period 1966-95; adopting a reduced-form approach rather than holding to a particular structural model; distinguishing between within-country and cross-country effects; and employing a class of estimators that controls for the problems of simultaneity and reverse causation. Among their findings: Current account deficits in developing countries are moderately persistent; A rise in domestic output growth generates a larger current account deficit; Increases in savings rates have a positive effect on the current account; Shocks that increase the terms of trade or cause the real exchange rate to appreciate are linked with higher current account deficits; Either higher growth rates in industrial economies or higher international interest rates reduce the current account deficit in developing economies. This paper-a product of the Regional Studies Program, Latin America and the Caribbean Region-is part of an effort in the region to understand the determinants of external sustainability. The authors may be contacted at crcn@troi.cc.rochester.edu, achong@worldbank.org, or nloayza@condor.bcentral.cl.

Buffer --- Business Cycle --- Central Bank --- Consumption --- Cross-Country Studies --- Currencies and Exchange Rates --- Current Account --- Current Account Balance --- Current Account Defic Current Account Deficits --- Current Account Position --- Debt Markets --- Demand --- Economy --- Emerging Markets --- Explanatory Variables --- External Debt --- Finance and Financial Sector Development --- Financial Literacy --- Interest Rates --- International Economics --- International Economics & Trade --- Macroeconomic Management --- Macroeconomic Variables --- Macroeconomics and Economic Growth --- National Income --- Private Saving --- Private Sector Development --- Surplus --- World Economy

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The authors examine the performance of small area welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, they compare predicted welfare indicators for a set of target populations with their true values. They construct target populations using actual data from a census of households in a set of rural Mexican communities. They examine estimates along three criteria: accuracy of confidence intervals, bias, and correlation with true values. The authors find that while point estimates are very stable, the precision of the estimates varies with alternative simulation methods. While the original approach of numerical gradient estimation yields standard errors that seem appropriate, some computationally less-intensive simulation procedures yield confidence intervals that are slightly too narrow. The precision of estimates is shown to diminish markedly if unobserved location effects at the village level are not well captured in underlying consumption models. With well specified models there is only slight evidence of bias, but the authors show that bias increases if underlying models fail to capture latent location effects. Correlations between estimated and true welfare at the local level are highest for mean expenditure and poverty measures and lower for inequality measures.

Capita Expenditure --- Degrees of Freedom --- Delta Method --- Econometrics --- Education --- Estimates of Poverty --- Explanatory Variables --- Finance and Financial Sector Development --- Financial Literacy --- Health, Nutrition and Population --- Household Survey --- Household Survey Data --- Households --- Macroeconomics and Economic Growth --- Parameter Estimates --- Population Census --- Population Policies --- Poverty Mapping --- Poverty Mapping Methodology --- Poverty Maps --- Poverty Measures --- Poverty Reduction --- Pro-Poor Growth --- Rural Development --- Rural Poverty Reduction --- Science and Technology Development --- Science Education --- Scientific Research and Science Parks --- Simulation Procedures --- Simulations --- Small Area Estimation --- Small Area Estimation Poverty Mapping --- Standard Deviation --- Standard Errors --- Statistical and Mathematical Sciences --- Variance-Covariance Matrix